Abstract
The growing demand for broadband communications anytime, anywhere has paved the way to the usage of Unmanned Aerial Vehicles (UAVs) for providing Internet access in areas without network infrastructure and enhancing the performance of existing networks. However, the usage of Flying Multi-hop Networks (FMNs) in such scenarios brings up significant challenges concerning network routing, in order to permanently provide the Quality of Service expected by the users. The problem is exacerbated in crowded events, where the FMN may be formed by many UAVs to address the traffic demand, causing interflow interference within the FMN. Typically, estimating inter-flow interference is not straightforward and requires the exchange of probe packets, thus increasing network overhead. The main contribution of this paper is an inter-flow interference-aware routing metric, named I2R, designed for centralized routing in FMNs with controllable topology. I2R does not require any control packets and enables the configuration of paths with minimal Euclidean distance formed by UAVs with the lowest number of neighbors in carrier-sense range, thus minimizing inter-flow interference in the FMN. Simulation results show the I2R superior performance, with significant gains in terms of throughput and end-to-end delay, when compared with state of the art routing metrics.

Abstract
Cascaded utilization of natural gas, electric power, and heat could leverage synergetic effects among these energy resources, precipitating the advent of integrated energy systems. In such infrastructures, energy hub is an interface among different energy systems, playing the role of energy production, conversion and storage. The capacity of energy hub largely determines how tightly these energy systems are coupled and how flexibly the whole system would behave. This paper proposes a data-driven two-stage robust stochastic programming model for energy hub capacity planning with distributional robustness guarantee. Renewable generation and load uncertainties are modelled by a family of ambiguous probability distributions near an empirical distribution in the sense of Kullback-Leibler (KL) divergence measure. The objective is to minimize the sum of the construction cost and the expected life-cycle operating cost under the worst-case distribution restricted in the ambiguity set. Network energy flow in normal operating conditions is considered; demand supply reliability in extreme conditions is taken into account via robust chance constraints. Through duality theory and sampling average approximation, the proposed model is transformed into an equivalent convex program with a nonlinear objective and linear constraints, and is solved by an outer-approximation algorithm which entails solving only linear program. Case studies demonstrate the effectiveness of the proposed model and method. © 2019 IEEE.

Abstract
Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods. Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called SegOptim, in which several segmentation algorithms are interfaced, mostly from open-source software (GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS, TerraLib), but also from proprietary software (ESRI ArcGIS). SegOptim also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest. We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03-0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10-20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective. Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the SegOptim approach (and potential solutions) as well as a future roadmap to expand its current functionalities.

Abstract
The increase volume of vehicles circulating in large cities and the limited space for parking are factors that motivate the adoption of systems capable of dealing with such problems. In this context, smart parking systems are suitable solutions to avoid the traffic congestion, the air pollution and the long search to find a free parking spot. The inclusion of emergent ICT technologies and artificial intelligence techniques, and particularly using multi-agent systems, combined under the scope of Cyber-Physical Systems (CPS), ensure flexibility, modularity, adaptability and the decentralization of intelligence through autonomous, cooperative and proactive entities. Such smart parking systems can be easily adapted to any type of vehicle to be parked and scalable in terms of the number of parking spots and drivers/vehicles. A fundamental issue in these agent-based CPS parking systems is the interconnection between the cyber and physical counterparts, i.e. between the software agents and the physical asset controllers to access the parking spots. This paper focuses on developing an agent-based CPS for a smart parking system and particularly addressing how the software agents are interconnected with the physical asset controllers using proper Internet of Things technologies. The proposed approach was implemented in two distinct parking systems, one for bicycles and another for cars, showing an efficient, modular, adaptable and scalable operation.

Abstract
Distillers' dried grains with solubles (DDGS) has low tryptophan (Trp) relatively to the branched-chain amino acids (BCAA) levels, and this may reduce transport of Trp through the blood-brain barrier due to competition for the same transport carrier. This may affect synthesis and release of serotonin, with negative consequences in stress tolerance. In the present study, it is hypothesized that a Trp/BCAA unbalance in high DDGS diets may impair the capacity of gilthead seabream (Sparus aurata) juveniles to cope with chronic stress induced by high stocking density. Three DDGS-based diets (30%DDGS+13%FM) were formulated and supplemented with Trp at 0, 0.13, and 0.25% of the diet and tested in triplicate, at two initial stocking densities (5 and 16 kg m(-3)), in a 2 x 3 total randomized factorial design. The growth trial was performed with 12 g fish and lasted 64 days. Irrespective of the diet, high stocking density reduced growth performance and feed intake, but not feed efficiency. Plasma protein, triglycerides, and cholesterol levels; whole-body lipid, hepatosomatic index, and liver glycogen; hepatic activity of key-enzymes of glycolysis and lipogenesis were also reduced. Moreover, plasma glucose level and hepatic activity of key-enzymes gluconeogenesis were increased. Irrespective of stocking density, diets supplementation with Trp did not affect growth and feed efficiency, but increased hepatic lipase activity and reduced liver lipids, plasma triglycerides and cholesterol levels, and hepatic activity of key-enzymes of amino acid catabolism. Moreover, dietary Trp supplementation restored plasma glucose levels of fish kept at high stocking density to levels similar to that of fish kept at low stocking density. Overall, present results indicate that high stocking density reduced growth performance without affecting feed efficiency of gilthead seabream. Dietary Trp supplementation did not counteract the negative effect of stocking density on growth performance but seemed to mitigate stress response of gilthead seabream juveniles kept at high stocking density.

Abstract
The evolution of society can be related to industrial revolutions. Revolutions are disruptive and transformative phenomena that change and interact with several systems. Industrial revolutions depend on changes in scientific, and mostly technological, paradigms and require people's participation. They are not only created with individual political intentions, because they are collective and complex systems. The expression Industry 4.0, created in Germany in 2011, denotes the so-called fourth industrial revolution. The question considered in this paper is whether Industry 4.0, as the fourth industrial revolution, is effectively underway or is it still only a vision of the future? This article analyses, from the point of view of the science of complexity, the transformations and the relations of industrial systems with other selected systems. It was made through fractal analysis using indicators of four countries, namely, United Kingdom, United States of America, Germany and China. Considering the evolution of population growth, Gross Domestic Product per capita, communication technologies and intellectual property, the results of the analysis show that the factor that stands out is the protection of intellectual property. The analysis of the previous indicators showed that it is not possible to claim that the fourth industrial revolution is underway, implying that Industrial 4.0 may stil be a vision of the future. The results obtained can not be considered conclusive and more research is needed.